Method for predicting aerodynamic force of maglev train, operation control device and storage medium

Abstract

The application provides a maglev train aerodynamic force prediction method, an operation control device and a storage medium. The maglev train aerodynamic force prediction method comprises the following steps: obtaining modeling data of the maglev train, wherein the modeling data comprises first aerodynamic force data under different elevated heights, second aerodynamic force data under different vehicle speeds and third aerodynamic force data under different wind speeds; establishing an aerodynamic force prediction model according to the first aerodynamic force data, the second aerodynamic force data and the third aerodynamic force data; obtaining a real-time elevated height, a real-time vehicle speed and a real-time wind speed of the maglev train; and obtaining predicted aerodynamic force based on the aerodynamic force prediction model and according to the real-time elevated height, the real-time vehicle speed and the real-time wind speed. According to the technical scheme of the application, the predicted aerodynamic force with high precision can be obtained, and the operation safety and the riding comfort of the maglev train can be improved.

Description

Technical Field

[0001] This invention relates to the field of rail transit technology, and in particular to a method for predicting the aerodynamic forces of a maglev train, an operation control device, and a storage medium. Background Technology

[0002] Maglev trains have broad application prospects. To ensure the safe operation of maglev trains, it is usually necessary to conduct simulations to provide technical support for operation control. However, due to limitations in computing resources and time, and the fact that maglev trains are often affected by various complex factors such as environmental factors and operating conditions during actual operation, it is difficult to conduct comprehensive numerical simulations of the actual operating conditions of maglev trains. This makes it impossible to accurately predict the aerodynamic forces of maglev trains under different operating conditions, which affects the safety and comfort of maglev train operation. Summary of the Invention

[0003] This invention aims to solve at least one of the technical problems existing in the prior art. To this end, this invention proposes a method for predicting aerodynamic forces of maglev trains, an operation control device, and a storage medium, which can obtain highly accurate predicted aerodynamic forces, thereby improving the operational safety and passenger comfort of maglev trains.

[0004] In a first aspect, embodiments of the present invention provide a method for predicting the aerodynamic forces of a maglev train, comprising:

[0005] The modeling data of the maglev train is obtained, wherein the modeling data includes first aerodynamic data at different elevation heights, second aerodynamic data at different speeds, and third aerodynamic data at different wind speeds;

[0006] An aerodynamic prediction model is established based on the first aerodynamic data, the second aerodynamic data, and the third aerodynamic data.

[0007] Obtain the current real-time elevated height, real-time vehicle speed, and real-time wind speed of the maglev train;

[0008] Based on the aforementioned aerodynamic prediction model, the predicted aerodynamic forces are obtained according to the real-time elevated height, real-time vehicle speed, and real-time wind speed.

[0009] The aerodynamic prediction method for maglev trains provided by the embodiments of the present invention has at least the following beneficial effects: By acquiring the first, second, and third aerodynamic data of the maglev train, and considering different influencing factors from multiple perspectives, it can more closely approximate the operating conditions of the maglev train. Furthermore, an aerodynamic prediction model is established based on the first, second, and third aerodynamic data. This model can predict the aerodynamic forces of the maglev train based on the relationship between elevation height, vehicle speed, and wind speed, facilitating multi-faceted simulation of the actual operating conditions of the maglev train. Simultaneously, establishing the aerodynamic prediction model can significantly save simulation time and economic costs. The prediction process is simple and convenient. By acquiring the current real-time elevation height, real-time vehicle speed, and real-time wind speed of the maglev train and inputting them into the aerodynamic prediction model, a highly accurate predicted aerodynamic force can be obtained, which is beneficial for improving the operational safety and passenger comfort of the maglev train.

[0010] In the above-mentioned aerodynamic prediction method for maglev trains, the step of establishing an aerodynamic prediction model based on the first aerodynamic data, the second aerodynamic data, and the third aerodynamic data includes:

[0011] A first functional relationship between the elevated height ratio and the first predicted aerodynamic force is established based on the first aerodynamic force data, wherein the elevated height ratio is used to reflect the ratio of the first aerodynamic force data to the elevated height under the same wind speed and vehicle speed.

[0012] A second functional relationship between the vehicle speed ratio and the second predicted aerodynamic force is established based on the second aerodynamic force data, wherein the vehicle speed ratio is used to reflect the ratio of the second aerodynamic force data to the vehicle speed under the same elevated height and wind speed.

[0013] A third functional relationship between the wind speed ratio and the third predicted aerodynamic force is established based on the third aerodynamic force data, wherein the wind speed ratio is used to reflect the ratio of the third aerodynamic force data to the wind speed under the same vehicle speed and elevated height.

[0014] The first functional relationship, the second functional relationship, and the third functional relationship are fitted to obtain a well-established aerodynamic prediction model.

[0015] In the above-mentioned aerodynamic force prediction method for maglev trains, the first aerodynamic force data includes the aerodynamic forces of the first car, the second car, and the third car. The step of establishing a first functional relationship between the elevated height ratio and the first predicted aerodynamic force based on the first aerodynamic force data includes:

[0016] The aerodynamic forces of the first car body, the second car body, and the third car body are converted into corresponding first dimensionless coefficients, wherein the first dimensionless coefficients include drag coefficient, lateral force coefficient, lift coefficient, and overturning moment coefficient.

[0017] The first height ratio, second height ratio, and third height ratio are determined based on the first dimensionless coefficient and the elevated height of each carriage.

[0018] The expression for the first functional relationship is obtained by adding the first height ratio, the second height ratio, the third height ratio, the first preset influence value, and the second preset influence value.

[0019] In the above-mentioned aerodynamic force prediction method for maglev trains, the second aerodynamic force data includes the aerodynamic forces of the fourth car, the fifth car, and the sixth car. The step of establishing a second functional relationship between the speed ratio and the second predicted aerodynamic force based on the second aerodynamic force data includes:

[0020] The aerodynamic forces of the fourth, fifth, and sixth carriages are converted into corresponding second dimensionless coefficients, wherein the second dimensionless coefficients include drag coefficient, lateral force coefficient, lift coefficient, and overturning moment coefficient.

[0021] The corresponding first speed ratio, second speed ratio, and third speed ratio are determined based on the second dimensionless coefficient and speed of each car.

[0022] The expression for the second functional relationship is obtained by adding the first vehicle speed ratio, the second vehicle speed ratio, the third vehicle speed ratio, the first preset influence value, and the second preset influence value.

[0023] In the above-mentioned aerodynamic prediction method for maglev trains, the third aerodynamic data includes the aerodynamic forces of the seventh car, the eighth car, and the ninth car. The step of establishing a third functional relationship between the wind speed ratio and the third predicted aerodynamic force based on the third aerodynamic data includes:

[0024] The aerodynamic forces of the seventh, eighth, and ninth carriages are converted into corresponding third dimensionless coefficients, wherein the third dimensionless coefficients include drag coefficient, lateral force coefficient, lift coefficient, and overturning moment coefficient.

[0025] The corresponding first wind speed ratio, second wind speed ratio, and third wind speed ratio are determined based on the third dimensionless coefficient and wind speed of each carriage.

[0026] The expression for the third function relationship is obtained by adding the first wind speed ratio, the second wind speed ratio, the third wind speed ratio, the first preset influence value, and the second preset influence value.

[0027] In the above-mentioned aerodynamic prediction method for maglev trains, the fitting process of the first functional relationship, the second functional relationship, and the third functional relationship to obtain the established aerodynamic prediction model includes:

[0028] The first functional relationship, the second functional relationship, and the third functional relationship are fitted using a polynomial fitting method to obtain a polynomial equation;

[0029] When the fitting degree of the polynomial equation reaches the preset fitting degree, a well-established aerodynamic prediction model is obtained.

[0030] In the above-mentioned aerodynamic prediction method for maglev trains, the polynomial equation is a cubic equation in one variable. The step of the polynomial equation achieving a preset fitting degree includes:

[0031] When the absolute difference between the fitting coefficient of the cubic equation and the preset coefficient is less than or equal to the preset difference, the fitting coefficient is used to represent the degree of fitting of the cubic equation.

[0032] The aforementioned method for predicting the aerodynamic forces of maglev trains also includes:

[0033] Multiple predicted aerodynamic forces are acquired at preset time intervals;

[0034] The predicted variation curves of the maglev train are constructed based on multiple predicted aerodynamic forces.

[0035] In a second aspect, embodiments of the present invention provide an operation control device, including at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform the aerodynamic prediction method for maglev trains as described in the first aspect embodiment above.

[0036] The operation control device provided by the embodiments of the present invention has at least the following beneficial effects: by acquiring the first aerodynamic data, the second aerodynamic data, and the third aerodynamic data of the maglev train, and considering different influencing factors from multiple perspectives, it can more closely approximate the operating conditions of the maglev train. Furthermore, an aerodynamic prediction model is established based on the first, second, and third aerodynamic data. This aerodynamic prediction model can predict the aerodynamic forces of the maglev train based on the relationship between elevation height, vehicle speed, and wind speed, facilitating multi-faceted simulation of the actual operating conditions of the maglev train. Simultaneously, establishing the aerodynamic prediction model can greatly save simulation time and economic costs. The prediction process is simple and convenient. By acquiring the current real-time elevation height, real-time vehicle speed, and real-time wind speed of the maglev train and inputting them into the aerodynamic prediction model, highly accurate predicted aerodynamic forces can be obtained, which is beneficial for improving the operational safety and passenger comfort of the maglev train.

[0037] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the aerodynamic prediction method for maglev trains as described in the first aspect of the embodiments above.

[0038] The computer-readable storage medium provided in the embodiments of the present invention has at least the following beneficial effects: by acquiring the first aerodynamic data, the second aerodynamic data, and the third aerodynamic data of the maglev train, and considering different influencing factors from multiple perspectives, it can more closely approximate the operating conditions of the maglev train. Furthermore, an aerodynamic prediction model can be established based on the first, second, and third aerodynamic data. This aerodynamic prediction model can predict the aerodynamic forces of the maglev train based on the relationship between elevation height, vehicle speed, and wind speed, facilitating multi-faceted simulation of the actual operating conditions of the maglev train. Simultaneously, establishing the aerodynamic prediction model can greatly save simulation time and economic costs. The prediction process is simple and convenient. By acquiring the current real-time elevation height, real-time vehicle speed, and real-time wind speed of the maglev train and inputting them into the aerodynamic prediction model, highly accurate predicted aerodynamic forces can be obtained, which is beneficial for improving the operational safety and passenger comfort of the maglev train.

[0039] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description and the drawings. Attached Figure Description

[0040] The present invention will be further described below with reference to the accompanying drawings and embodiments;

[0041] Figure 1 This is a flowchart of the aerodynamic prediction method for maglev trains provided in Embodiment 1 of the present invention.

[0042] Figure 2 This is a flowchart of the aerodynamic prediction method for maglev trains provided in Embodiment 2 of the present invention;

[0043] Figure 3 This is a flowchart of the aerodynamic prediction method for maglev trains provided in Embodiment 3 of the present invention;

[0044] Figure 4 This is a flowchart of the aerodynamic prediction method for maglev trains provided in Embodiment 4 of the present invention;

[0045] Figure 5 This is a flowchart of the aerodynamic prediction method for maglev trains provided in Embodiment 5 of the present invention;

[0046] Figure 6 This is a flowchart of the aerodynamic prediction method for maglev trains provided in Embodiment Six of the present invention;

[0047] Figure 7 This is a flowchart of the aerodynamic prediction method for maglev trains provided in Embodiment 7 of the present invention;

[0048] Figure 8 This is a schematic diagram of the operation control device provided in Embodiment 8 of the present invention. Detailed Implementation

[0049] This section will describe in detail specific embodiments of the present invention. Preferred embodiments of the present invention are shown in the accompanying drawings. The purpose of the drawings is to supplement the textual description with graphics, so that people can intuitively and vividly understand each technical feature and overall technical solution of the present invention, but they should not be construed as limiting the scope of protection of the present invention.

[0050] It should be understood that in the description of the embodiments of the present invention, the use of terms such as "first" and "second" is only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of technical features indicated, or implicitly indicating the order of the technical features indicated. "At least one" means one or more; "more than" means two or more; "greater than," "less than," and "exceeding" are understood to exclude the stated number; "above," "below," and "within" are understood to include the stated number; "several" means one or more, unless otherwise explicitly defined. "And / or" describes the relationship between related objects, indicating that three relationships can exist. It can be understood that A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. Where A and B can be singular or plural.

[0051] Furthermore, unless otherwise explicitly specified and limited, the term "connection / linkage" should be interpreted broadly. For example, it can be a fixed or movable connection, a detachable or non-detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or a connection that allows communication between them; it can be a direct connection or an indirect connection through an intermediate medium. It should be noted that although a logical sequence is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown in the flowchart.

[0052] It should be noted that the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0053] The aerodynamic prediction method, operation control device, and storage medium for maglev trains provided in this invention can obtain highly accurate predicted aerodynamic forces, which is beneficial to improving the operation safety and ride comfort of maglev trains.

[0054] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0055] like Figure 1 As shown, an embodiment of the first aspect of the present invention provides a method for predicting the aerodynamic forces of a maglev train, including but not limited to steps S110 to S140:

[0056] Step S110: Obtain the modeling data of the maglev train, which includes the first aerodynamic data at different elevation heights, the second aerodynamic data at different speeds, and the third aerodynamic data at different wind speeds.

[0057] It should be noted that, in order to conduct a comprehensive numerical simulation of the actual operating conditions of the maglev train and obtain modeling data that can reflect different influencing factors, specifically, we need to obtain the first aerodynamic force data at different elevated heights under the same wind speed and train speed, the second aerodynamic force data at different train speeds under the same elevated height and wind speed, and the third aerodynamic force data at different wind speeds under the same train speed and elevated height.

[0058] Step S120: Establish an aerodynamic prediction model based on the first aerodynamic data, the second aerodynamic data, and the third aerodynamic data;

[0059] Since the first, second, and third aerodynamic data take into account the effects of elevation height, vehicle speed, and wind speed on the maglev train, the established aerodynamic prediction model can predict the aerodynamic forces of the maglev train based on the relationship between elevation height, vehicle speed, and wind speed.

[0060] Specifically, the Star-CCM+ software can be used to establish an aerodynamic prediction model.

[0061] Step S130: Obtain the current real-time elevated height, real-time speed and real-time wind speed of the maglev train;

[0062] Step S140: Based on the aerodynamic prediction model, the predicted aerodynamic forces are obtained according to the real-time elevated height, real-time vehicle speed and real-time wind speed.

[0063] Specifically, by inputting the real-time elevated height, real-time speed, and real-time wind speed of the maglev train into the aerodynamic prediction model, the predicted aerodynamic forces of the maglev train can be obtained.

[0064] It should be noted that, due to the complex terrain in my country, most of the routes traversed by maglev trains are elevated. As the elevation increases, the ambient wind speed increases exponentially, which will affect the safety, stability, and comfort of maglev train operation. Therefore, by analyzing the first aerodynamic data at different elevation heights, the influence of elevation height on the aerodynamic forces of maglev trains can be considered, which will facilitate a more accurate prediction of aerodynamic forces.

[0065] Furthermore, as people's living standards continue to improve, the demand for high speeds during the operation of maglev trains is also gradually increasing. Therefore, by analyzing the secondary aerodynamic data at different speeds and considering the impact of speed on the aerodynamics of maglev trains, it is beneficial to improve the user experience.

[0066] In addition, compared to a windless environment, the operational safety and ride comfort of maglev trains operating in crosswind environments will deteriorate. Therefore, by analyzing the third aerodynamic force data under different wind speeds and considering the impact of wind speed on the aerodynamic forces of maglev trains, it is easier to obtain more accurate predictions of aerodynamic forces, which is beneficial to improving the operational safety and ride comfort of maglev trains.

[0067] The aerodynamic prediction method for maglev trains provided in the first aspect of the embodiment above, by acquiring first, second, and third aerodynamic data of the maglev train and considering different influencing factors from multiple perspectives, can more closely approximate the operating conditions of the maglev train. An aerodynamic prediction model is established based on the first, second, and third aerodynamic data. This model can predict the aerodynamic forces of the maglev train based on the relationship between elevation height, vehicle speed, and wind speed, facilitating multi-faceted simulation of the maglev train's actual operating conditions. Furthermore, establishing the aerodynamic prediction model can significantly save simulation time and economic costs. The prediction process is simple and convenient; by acquiring the maglev train's current real-time elevation height, real-time vehicle speed, and real-time wind speed and inputting them into the aerodynamic prediction model, highly accurate predicted aerodynamic forces can be obtained, which is beneficial for improving the operational safety and passenger comfort of the maglev train.

[0068] It should be noted that the aerodynamic prediction method for maglev trains in this embodiment of the invention can predict the magnitude of aerodynamic forces of maglev trains under the relationship of elevation height, train speed, and wind speed. The error between the prediction result and the actual result is within a reasonable range, and the prediction accuracy is good. It can provide effective data support for the development research on the safety performance and ride comfort of maglev trains operating at different speeds and elevation heights in crosswind environments.

[0069] like Figure 2 As shown, in the above-mentioned aerodynamic prediction method for maglev trains, step S120 establishes an aerodynamic prediction model based on the first aerodynamic data, the second aerodynamic data, and the third aerodynamic data, including but not limited to steps S210 to S240:

[0070] Step S210: Establish a first functional relationship between the elevated height ratio and the first predicted aerodynamic force based on the first aerodynamic force data, wherein the elevated height ratio is used to reflect the ratio of the first aerodynamic force data to the elevated height under the same wind speed and vehicle speed.

[0071] Step S220: Establish a second functional relationship between the vehicle speed ratio and the second predicted aerodynamic force based on the second aerodynamic force data, wherein the vehicle speed ratio is used to reflect the ratio of the second aerodynamic force data to the vehicle speed under the same elevated height and wind speed.

[0072] Step S230: Establish a third functional relationship between the wind speed ratio and the third predicted aerodynamic force based on the third aerodynamic force data, wherein the wind speed ratio is used to reflect the ratio of the third aerodynamic force data to the wind speed under the same vehicle speed and elevated height.

[0073] Step S240: Fit the first functional relationship, the second functional relationship, and the third functional relationship to obtain the established aerodynamic prediction model.

[0074] In this embodiment, the ratio of the first aerodynamic force data to the elevated height is determined under the same wind speed and vehicle speed conditions. A first functional relationship is established between the elevated height ratio and the first predicted aerodynamic force. Based on the first functional relationship, the magnitude of the aerodynamic force of the maglev train under the elevated height factor can be determined. The ratio of the second aerodynamic force data to the vehicle speed is determined under the same elevated height and wind speed conditions. A second functional relationship is established between the vehicle speed ratio and the second predicted aerodynamic force. Based on the second functional relationship, the magnitude of the aerodynamic force of the maglev train under the vehicle speed factor can be determined. The ratio of the third aerodynamic force data to the wind speed is determined under the same vehicle speed and elevated height conditions. Based on the third functional relationship, the magnitude of the aerodynamic force of the maglev train under the wind speed factor can be determined. By fitting the first, second, and third functional relationships, a well-established aerodynamic force prediction model can be obtained, which can predict the aerodynamic force of the maglev train based on the elevated height-vehicle speed-wind speed relationship with high prediction accuracy.

[0075] Specifically, the fitting of function relationships can be performed using software such as MATLAB, Excel, and Origin.

[0076] It should be noted that the predicted aerodynamic forces include the predicted drag, predicted lateral force, predicted lift, and predicted overturning moment.

[0077] like Figure 3 As shown, in the above-mentioned aerodynamic prediction method for maglev trains, the first aerodynamic data includes the aerodynamic forces of the first car, the second car, and the third car. In step S210, a first functional relationship is established between the elevated height ratio and the first predicted aerodynamic force based on the first aerodynamic data, including but not limited to steps S310 to S330:

[0078] Step S310: Convert the aerodynamic forces of the first car body, the second car body, and the third car body into corresponding first dimensionless coefficients, wherein the first dimensionless coefficients include drag coefficient, lateral force coefficient, lift coefficient, and overturning moment coefficient.

[0079] Step S320: Determine the corresponding first height ratio, second height ratio, and third height ratio based on the first dimensionless coefficient and the elevated height of each carriage;

[0080] Step S330: Add the first height ratio, the second height ratio, the third height ratio, the first preset influence value, and the second preset influence value to obtain the expression of the first functional relationship.

[0081] In this embodiment, the aerodynamic forces of the first, second, and third carriages are the aerodynamic forces corresponding to the first, middle, and last carriages of the maglev train, respectively. These are aerodynamic force data at different elevation heights under the same wind speed and train speed. The aerodynamic forces of the first, second, and third carriages are converted into first dimensionless coefficients corresponding to the three carriages. Based on the ratio of the first dimensionless coefficients corresponding to each carriage to the elevation height, the first height ratio, second height ratio, and third height ratio can be obtained. By adding the first height ratio, second height ratio, third height ratio, first preset influence value, and second preset influence value, the expression of the first functional relationship is obtained, that is, the first functional relationship between the elevation height ratio and the first predicted aerodynamic force is established. This facilitates the determination of the magnitude of the aerodynamic force of the maglev train under the elevation height factor, fully considers the influence of different elevation heights on the aerodynamic force of the maglev train, and is conducive to improving the safety, stability, and comfort of the maglev train operation. At the same time, by analyzing the aerodynamic influence law of multiple carriages of the maglev train, the efficiency and accuracy of aerodynamic force prediction can be improved.

[0082] It should be noted that aerodynamic forces include drag, lateral force, lift, and overturning moment. The first dimensionless coefficient includes the drag coefficient, lateral force coefficient, lift coefficient, and overturning moment coefficient, corresponding to aerodynamic forces in different dimensions.

[0083] The aerodynamic forces of the first, second, and third carriages are converted into their corresponding first dimensionless coefficients in the same way. Taking the aerodynamic force of the first carriage as an example, the corresponding first dimensionless coefficient can be obtained according to the following aerodynamic force conversion formula:

[0084]

[0085]

[0086]

[0087]

[0088] Among them, C x C s C l and These are the drag coefficient, lateral force coefficient, lift coefficient, and overturning moment coefficient, respectively, i.e., the first dimensionless coefficient, F. x F s F l , The values ​​represent the drag, lateral force, lift, and overturning moment of the maglev train, i.e., the aerodynamic force of the first carriage. ρ represents the air density, taken as 1.225 kg / m³. S x and S y Sz V represents the projected area of ​​the cross-section, longitudinal section, and vertical section of the maglev train, respectively. T Let H be the vehicle speed, H be the characteristic length, and H be the height of the maglev train.

[0089] Specifically, this invention establishes an aerodynamic prediction model for a three-car maglev train, namely, the first, middle, and last cars, which can be denoted as car 1, car 2, and car 3, respectively. The elevated height ratio α includes a first height ratio, a second height ratio, and a third height ratio, wherein... The first height ratio is the ratio of the first dimensionless coefficient corresponding to carriage 1 to the elevated height h, i.e., α1. The second height ratio is the ratio of the first dimensionless coefficient corresponding to carriage 2 to the elevated height h, i.e., α2. The third height ratio is the ratio of the first dimensionless coefficient corresponding to carriage 3 to the elevated height h, i.e., α3.

[0090] Under the same wind speed and vehicle speed, a first functional relationship is established between the ratio of elevated height and the first predicted aerodynamic force. The expression of the first functional relationship is as follows:

[0091] F α (x)=α1(x)+α2(x)+α3(x)+λ1+η3;

[0092] Among them, F α α(x) represents the first predicted aerodynamic force, α1(x) represents the individual effect of car 1 on the aerodynamic drag of the rear carriage, α2(x) represents the individual effect of car 2 on the aerodynamic drag of both the rear and front carriages, and α3(x) represents the individual effect of car 3 on the aerodynamic drag of the front carriage. λ1 represents the first preset influence value, which is the interactive effect of the rear carriages 2 and 3 on the aerodynamic drag of the front carriage 1. η3 represents the second preset influence value, which is the interactive effect of the front carriages 1 and 2 on the aerodynamic drag of the rear carriage 3.

[0093] like Figure 4 As shown, in the above-mentioned aerodynamic prediction method for maglev trains, the second aerodynamic data includes the aerodynamic forces of the fourth car, the fifth car, and the sixth car. In step S220, a second functional relationship between the speed ratio and the second predicted aerodynamic force is established based on the second aerodynamic data, including but not limited to steps S410 to S430:

[0094] Step S410: Convert the aerodynamic forces of the fourth, fifth, and sixth car bodies into corresponding second dimensionless coefficients, wherein the second dimensionless coefficients include drag coefficient, lateral force coefficient, lift coefficient, and overturning moment coefficient.

[0095] Step S420: Determine the corresponding first speed ratio, second speed ratio, and third speed ratio based on the second dimensionless coefficient and speed of each car.

[0096] Step S430: Add the first vehicle speed ratio, the second vehicle speed ratio, the third vehicle speed ratio, the first preset influence value, and the second preset influence value to obtain the expression of the second function relationship.

[0097] In this embodiment, the aerodynamic forces of the fourth, fifth, and sixth cars are the aerodynamic forces corresponding to the first, middle, and last cars of the maglev train, respectively. These are aerodynamic force data at different speeds under the same elevation and wind speed. The aerodynamic forces of the fourth, fifth, and sixth cars are converted into second dimensionless coefficients corresponding to the three cars. Based on the ratio of the second dimensionless coefficients corresponding to each car to the train speed, the first speed ratio, second speed ratio, and third speed ratio can be obtained. The first speed... The expression for the second functional relationship is obtained by adding the ratio, the second speed ratio, the third speed ratio, the first preset influence value, and the second preset influence value. That is, the second functional relationship between the speed ratio and the second predicted aerodynamic force is established, which makes it easier to determine the magnitude of the aerodynamic force of the maglev train under the speed factor. It fully considers the influence of different speeds on the aerodynamic force of the maglev train, which is conducive to improving the safety, stability and comfort of the maglev train operation. At the same time, by analyzing the aerodynamic influence law of multiple carriages of the maglev train, the efficiency and accuracy of aerodynamic force prediction can be improved.

[0098] It should be noted that the aerodynamic forces of the fourth, fifth, and sixth carriages are converted into the corresponding second dimensionless coefficients in the same way as the aerodynamic forces of the first carriage are converted into the corresponding first dimensionless coefficients in the above embodiment, and will not be repeated here.

[0099] Specifically, this invention establishes an aerodynamic prediction model for a maglev train with three carriages: the first, middle, and last carriages, which can be denoted as carriage 1, carriage 2, and carriage 3, respectively. The speed ratio β includes a first speed ratio, a second speed ratio, and a third speed ratio, wherein... The first speed ratio is the second dimensionless coefficient corresponding to carriage 1 and the vehicle speed V. T The ratio, i.e. β1, is the second speed ratio, which is the second dimensionless coefficient corresponding to carriage 2 and the vehicle speed V. T The ratio, i.e. β2, is the third speed ratio, which is the second dimensionless coefficient corresponding to car 3 and the speed V. T The ratio of , i.e. β2.

[0100] Under the same elevated height and wind speed conditions, a second functional relationship between the vehicle speed ratio and the second predicted aerodynamic force is established. The expression for the second functional relationship is as follows:

[0101] F β (x)=β1(x)+β2(x)+β3(x)+λ1+η3;

[0102] Among them, F β β(x) represents the second predicted aerodynamic force, β1(x) represents the individual effect of car 1 on the aerodynamic drag of the rear carriage, β2(x) represents the individual effect of car 2 on the aerodynamic drag of both the rear and front carriages, and β3(x) represents the individual effect of car 3 on the aerodynamic drag of the front carriage. λ1 represents the first preset influence value, which is the interactive effect of the rear carriages 2 and 3 on the aerodynamic drag of the front carriage 1. η3 represents the second preset influence value, which is the interactive effect of the front carriages 1 and 2 on the aerodynamic drag of the rear carriage 3.

[0103] like Figure 5 As shown, in the above-mentioned aerodynamic prediction method for maglev trains, the third aerodynamic data includes the aerodynamic forces of the seventh car, the eighth car, and the ninth car. In step S230, a third functional relationship between the wind speed ratio and the third predicted aerodynamic force is established based on the third aerodynamic data, including but not limited to steps S510 to S530:

[0104] Step S510: Convert the aerodynamic forces of the seventh, eighth, and ninth carriages into corresponding third dimensionless coefficients, wherein the third dimensionless coefficients include drag coefficient, lateral force coefficient, lift coefficient, and overturning moment coefficient.

[0105] Step S520: Determine the corresponding first wind speed ratio, second wind speed ratio, and third wind speed ratio based on the third dimensionless coefficient and wind speed of each carriage;

[0106] Step S530: Add the first wind speed ratio, the second wind speed ratio, the third wind speed ratio, the first preset influence value, and the second preset influence value to obtain the expression of the third function relationship.

[0107] In this embodiment, the aerodynamic forces of the seventh, eighth, and ninth carriages are the aerodynamic forces corresponding to the first, middle, and last carriages of the maglev train, respectively. These are aerodynamic force data for different wind speeds under the same train speed and elevated height. The aerodynamic forces of the seventh, eighth, and ninth carriages are converted into third dimensionless coefficients corresponding to the three carriages. Based on the ratio of the third dimensionless coefficients corresponding to each carriage to the wind speed, the first wind speed ratio, second wind speed ratio, and third wind speed ratio can be obtained. By using the first wind speed... The expression for the third functional relationship is obtained by adding the ratio, the second wind speed ratio, the third wind speed ratio, the first preset influence value, and the second preset influence value. That is, the third functional relationship between the wind speed ratio and the third predicted aerodynamic force is established. This makes it easier to determine the magnitude of the aerodynamic force of the maglev train under the influence of wind speed. It fully considers the influence of different wind speeds on the aerodynamic force of the maglev train, which is conducive to improving the safety, stability and comfort of the maglev train operation. At the same time, by analyzing the aerodynamic influence law of multiple carriages of the maglev train, the efficiency and accuracy of aerodynamic force prediction can be improved.

[0108] It should be noted that the aerodynamic forces of the seventh, eighth, and ninth carriages are converted into the corresponding third dimensionless coefficients in the same way as the aerodynamic forces of the first carriage are converted into the corresponding first dimensionless coefficients in the above embodiment, and will not be repeated here.

[0109] Specifically, this invention establishes an aerodynamic prediction model for a three-car maglev train, namely, the first, middle, and last cars, which can be denoted as car 1, car 2, and car 3, respectively. The wind speed ratio γ includes a first wind speed ratio, a second wind speed ratio, and a third wind speed ratio, wherein... The first wind speed ratio is the ratio of the third dimensionless coefficient corresponding to carriage 1 to the wind speed Vw, i.e., γ1. The second wind speed ratio is the ratio of the third dimensionless coefficient corresponding to carriage 2 to the wind speed Vw, i.e., γ2. The third wind speed ratio is the ratio of the third dimensionless coefficient corresponding to carriage 3 to the wind speed Vw, i.e., γ3.

[0110] Under the same vehicle speed and elevated height, a third functional relationship is established between the wind speed ratio and the third predicted aerodynamic force. The expression for the third functional relationship is as follows:

[0111] F γ (x)=γ1(x)+γ2(x)+γ3(x)+λ1+η3;

[0112] Among them, F γγ(x) represents the third predicted aerodynamic force, γ1(x) represents the individual effect of car 1 on the aerodynamic drag of the rear carriage, γ2(x) represents the individual effect of car 2 on the aerodynamic drag of both the rear and front carriages, and γ3(x) represents the individual effect of car 3 on the aerodynamic drag of the front carriage. λ1 represents the first preset influence value, which is the interactive effect of the rear carriages 2 and 3 on the aerodynamic drag of the front carriage 1. η3 represents the second preset influence value, which is the interactive effect of the front carriages 1 and 2 on the aerodynamic drag of the rear carriage 3.

[0113] like Figure 6 As shown, in the above-mentioned aerodynamic prediction method for maglev trains, step S240 involves fitting the first functional relationship, the second functional relationship, and the third functional relationship to obtain the established aerodynamic prediction model, including but not limited to steps S610 and S620:

[0114] Step S610: Use a polynomial fitting method to fit the first functional relationship, the second functional relationship, and the third functional relationship to obtain a polynomial equation;

[0115] Step S620: When the fitting degree of the polynomial equation reaches the preset fitting degree, a well-established aerodynamic prediction model is obtained.

[0116] In this embodiment, a polynomial equation is obtained by fitting the first, second, and third functional relationships. The degree of fitting of the polynomial equation is then determined to be reliable. When the degree of fitting of the polynomial equation reaches the preset degree of fitting, it indicates that the model has the best prediction effect. Thus, a well-established aerodynamic prediction model is obtained, which facilitates the prediction of the aerodynamic forces of maglev trains.

[0117] In the above-mentioned aerodynamic prediction method for maglev trains, the polynomial equation is a cubic equation in one variable. Step S620, when the fitting degree of the polynomial equation reaches a preset fitting degree, includes:

[0118] When the absolute difference between the fitting coefficients of a cubic equation and the preset coefficients is less than or equal to the preset difference, the fitting coefficients are used to represent the degree of fit of the cubic equation.

[0119] In this embodiment, the polynomial equation is a cubic equation in one variable. By determining the fitting coefficients of the cubic equation in one variable, when the absolute difference between the fitting coefficients of the cubic equation in one variable and the preset coefficients is less than or equal to the preset difference, it indicates that the current fitting degree is reliable, and a well-established aerodynamic prediction model is obtained.

[0120] Specifically, the form of a cubic equation in one variable is as follows:

[0121] y = ax 3 +bx2 +cx+d R 2 =0.998;

[0122] It should be noted that the coefficients a, b, c, and d are not fixed values ​​and can be adjusted according to actual needs. R 2 R is the fitting coefficient, 1 is the preset coefficient, and R is the fitting coefficient. 2 The closer to 1, the higher the fit and the more reliable it is.

[0123] like Figure 7 As shown, the above-mentioned aerodynamic prediction method for maglev trains also includes, but is not limited to, steps S710 and S720:

[0124] Step S710: Obtain multiple predicted aerodynamic forces at preset time intervals;

[0125] Step S720: Construct the predicted variation curve of the maglev train based on multiple predicted aerodynamic forces.

[0126] In this embodiment, multiple predicted aerodynamic forces are acquired at preset time intervals, and a predicted change curve of the maglev train is constructed. This facilitates the analysis of the change law of the predicted aerodynamic forces of the maglev train at different times and can provide intuitive and effective reference data.

[0127] like Figure 8 As shown, a second aspect of the present invention provides an operation control device 800, including at least one control processor 810 and a memory 820 for communicatively connecting to the at least one control processor 810; the control processor 810 and the memory 820 can be connected via a bus or other means. Figure 8 The diagram illustrates an example of a bus connection. Memory 820 stores instructions executable by at least one control processor 810. These instructions, when executed by the at least one control processor 810, enable the at least one control processor 810 to perform the maglev train aerodynamic prediction method as described in the first aspect embodiment above, for example, performing the above-described... Figure 1 Method steps S110 to S140 in the text Figure 2 Method steps S210 to S240 in the text Figure 3 Method steps S310 to S330 in the text Figure 4 Method steps S410 to S430, Figure 5 Method steps S510 to S530 in the text Figure 6 Method steps S610 and S620, and Figure 7The method steps S710 and S720 are described. By acquiring the first, second, and third aerodynamic data of the maglev train, and considering various influencing factors, the operating conditions of the maglev train can be more closely approximated. An aerodynamic prediction model is established based on the first, second, and third aerodynamic data. This model can predict the aerodynamic forces of the maglev train based on the relationship between elevation height, train speed, and wind speed, facilitating multi-faceted simulation of the maglev train's actual operating conditions. Furthermore, establishing this aerodynamic prediction model can significantly save simulation time and economic costs. The prediction process is simple and convenient; by acquiring the maglev train's current real-time elevation height, real-time train speed, and real-time wind speed and inputting them into the aerodynamic prediction model, highly accurate predicted aerodynamic forces can be obtained, which is beneficial for improving the operational safety and passenger comfort of the maglev train.

[0128] A third aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions that can be used to cause a computer to perform the aerodynamic prediction method for a maglev train as described in the first aspect above, for example, performing the above-described... Figure 1 Method steps S110 to S140 in the text Figure 2 Method steps S210 to S240 in the text Figure 3 Method steps S310 to S330 in the text Figure 4 Method steps S410 to S430, Figure 5 Method steps S510 to S530 in the text Figure 6 Method steps S610 and S620, and Figure 7 The method steps S710 and S720 are described. By acquiring the first, second, and third aerodynamic data of the maglev train, and considering various influencing factors, the operating conditions of the maglev train can be more closely approximated. An aerodynamic prediction model is established based on the first, second, and third aerodynamic data. This model can predict the aerodynamic forces of the maglev train based on the relationship between elevation height, train speed, and wind speed, facilitating multi-faceted simulation of the maglev train's actual operating conditions. Furthermore, establishing this aerodynamic prediction model can significantly save simulation time and economic costs. The prediction process is simple and convenient; by acquiring the maglev train's current real-time elevation height, real-time train speed, and real-time wind speed and inputting them into the aerodynamic prediction model, highly accurate predicted aerodynamic forces can be obtained, which is beneficial for improving the operational safety and passenger comfort of the maglev train.

[0129] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which may include computer storage media or non-transitory media and communication media or transient media. As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc DVD or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0130] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for predicting the aerodynamic forces of a maglev train, characterized in that, include: The modeling data of the maglev train is obtained, wherein the modeling data includes first aerodynamic data at different elevation heights, second aerodynamic data at different speeds, and third aerodynamic data at different wind speeds; An aerodynamic prediction model is established based on the first aerodynamic data, the second aerodynamic data, and the third aerodynamic data. Obtain the current real-time elevated height, real-time vehicle speed, and real-time wind speed of the maglev train; Based on the aerodynamic prediction model, the predicted aerodynamic forces are obtained according to the real-time elevated height, real-time vehicle speed and real-time wind speed. The step of establishing an aerodynamic prediction model based on the first aerodynamic data, the second aerodynamic data, and the third aerodynamic data includes: A first functional relationship between the elevated height ratio and the first predicted aerodynamic force is established based on the first aerodynamic data. The elevated height ratio reflects the ratio of the first aerodynamic data to the elevated height under the same wind speed and vehicle speed. The first aerodynamic data includes the aerodynamic forces of the first carriage, the second carriage, and the third carriage. Establishing the first functional relationship between the elevated height ratio and the first predicted aerodynamic force based on the first aerodynamic data includes the following steps: converting the first carriage aerodynamic forces, the second carriage aerodynamic forces, and the third carriage aerodynamic forces into corresponding first dimensionless coefficients, wherein the first dimensionless coefficients include drag coefficient, lateral force coefficient, lift coefficient, and overturning moment coefficient; determining the corresponding first height ratio, second height ratio, and third height ratio based on the first dimensionless coefficients and elevated height of each carriage; and adding the first height ratio, the second height ratio, the third height ratio, the first preset influence value, and the second preset influence value to obtain the expression for the first functional relationship. A second functional relationship between the vehicle speed ratio and the second predicted aerodynamic force is established based on the second aerodynamic force data. The vehicle speed ratio reflects the ratio of the second aerodynamic force data to the vehicle speed under the same elevated height and wind speed. The second aerodynamic force data includes the aerodynamic forces of the fourth, fifth, and sixth carriages. Establishing the second functional relationship between the vehicle speed ratio and the second predicted aerodynamic force based on the second aerodynamic force data includes the following steps: converting the aerodynamic forces of the fourth, fifth, and sixth carriages into corresponding second dimensionless coefficients, where the second dimensionless coefficients include drag coefficient, lateral force coefficient, lift coefficient, and overturning moment coefficient; determining the corresponding first, second, and third vehicle speed ratios based on the second dimensionless coefficients and vehicle speeds of each carriage; and adding the first, second, and third vehicle speed ratios, the third vehicle speed ratio, the first preset influence value, and the second preset influence value to obtain the expression for the second functional relationship. A third functional relationship between the wind speed ratio and the third predicted aerodynamic force is established based on the third aerodynamic force data. The wind speed ratio reflects the ratio of the third aerodynamic force data to the wind speed at the same vehicle speed and elevated height. The third aerodynamic force data includes the aerodynamic forces of the seventh, eighth, and ninth carriages. Establishing the third functional relationship between the wind speed ratio and the third predicted aerodynamic force based on the third aerodynamic force data includes the following steps: converting the aerodynamic forces of the seventh, eighth, and ninth carriages into corresponding third dimensionless coefficients, whereby the third dimensionless coefficients include drag coefficient, lateral force coefficient, lift coefficient, and overturning moment coefficient; determining the corresponding first, second, and third wind speed ratios based on the third dimensionless coefficients and wind speeds of each carriage; and adding the first, second, and third wind speed ratios, the third wind speed ratio, the first preset influence value, and the second preset influence value to obtain the expression for the third functional relationship. The first functional relationship, the second functional relationship, and the third functional relationship are fitted to obtain a well-established aerodynamic prediction model.

2. The aerodynamic prediction method for maglev trains according to claim 1, characterized in that, The fitting process of the first functional relationship, the second functional relationship, and the third functional relationship to obtain the established aerodynamic prediction model includes: The first functional relationship, the second functional relationship, and the third functional relationship are fitted using a polynomial fitting method to obtain a polynomial equation; When the fitting degree of the polynomial equation reaches the preset fitting degree, a well-established aerodynamic prediction model is obtained.

3. The aerodynamic prediction method for maglev trains according to claim 2, characterized in that, The polynomial equation is a cubic equation in one variable, and the step of the polynomial equation achieving a preset degree of fit includes: When the absolute difference between the fitting coefficient of the cubic equation and the preset coefficient is less than or equal to the preset difference, the fitting coefficient is used to represent the degree of fitting of the cubic equation.

4. The aerodynamic prediction method for maglev trains according to claim 1, characterized in that, Also includes: Multiple predicted aerodynamic forces are acquired at preset time intervals; The predicted variation curves of the maglev train are constructed based on multiple predicted aerodynamic forces.

5. An operation control device, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform the aerodynamic prediction method for maglev trains as described in any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform the aerodynamic prediction method for maglev trains as described in any one of claims 1 to 4.

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